housing-price-prediction-random-forest

Housing Price Prediction using Random Forest

This repository focuses on predicting housing prices using the Random Forest Regressor from sklearn.ensemble. The project highlights insights into feature importance, hyperparameter tuning, and leveraging ensemble learning techniques for accurate predictions.

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🎯 Purpose

The purpose of this project is to:

• Build a robust predictive model for housing price estimation using Random Forest.
• Understand the contribution of various features to housing prices.
• Enhance skills in ensemble modeling, feature importance analysis, and performance evaluation.

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📂 Dataset

Housing Prices Dataset

• Description: This dataset contains features influencing housing prices, such as area, number of rooms, and other amenities.
• Use Cases: Predictive modeling, feature engineering, and exploratory data analysis.
• Data Dictionary:
  • price: Selling price of the house in dollars. (Data Type: int64)
  • area: Total area of the property in square feet. (Data Type: int64)
  • bedrooms: Number of bedrooms in the property. (Data Type: int64)
  • bathrooms: Number of bathrooms in the property. (Data Type: int64)
  • stories: Number of floors in the house. (Data Type: int64)
  • mainroad: Whether the house is located on the main road (Yes/No). (Data Type: object)
  • guestroom: Availability of a guestroom (Yes/No). (Data Type: object)
  • basement: Presence of a basement (Yes/No). (Data Type: object)
  • hotwaterheating: Availability of hot water heating (Yes/No). (Data Type: object)
  • airconditioning: Availability of air conditioning (Yes/No). (Data Type: object)
  • parking: Number of parking spaces available. (Data Type: int64)
  • prefarea: Whether the house is located in a preferred area (Yes/No). (Data Type: object)
  • furnishingstatus: Level of furnishing (Fully, Semi, Unfurnished). (Data Type: object)
• File Reference: housing_prices.csv

  import pandas as pd
  url = 'https://github.com/vmahawar/data-science-datasets-collection/raw/main/housing_prices.csv'
  df = pd.read_csv(url)
  print(df.head())

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📊 Jupyter Notebook

Explore the complete analysis and model building process for predicting housing prices using Random Forest Regressor in the following notebook:

• housing-price-prediction-random-forest.ipynb

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🛠️ Tools and Libraries Used

This project leverages the following tools and libraries:

• Pandas: For data manipulation and preprocessing.
• Numpy: For numerical computations.
• Scikit-learn: For building and training the Random Forest Regressor.
• Matplotlib & Seaborn: For data visualization to explore feature relationships and data distributions.
• Jupyter Notebook: For developing and presenting the project interactively.

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🌟 Key Learnings

1. Feature Importance:

   • Analyzed feature significance using feature_importances_ from the Random Forest Regressor.
   • Highlighted key features like area, bedrooms, and stories that strongly influence housing prices.

2. Hyperparameter Tuning:

   • Experimented with parameters such as n_estimators, max_depth, and min_samples_split to optimize model performance.
   • Observed the impact of different parameter settings on prediction accuracy.

3. Evaluation Metrics:

   • Assessed model performance using metrics such as R-squared and Mean Absolute Error (MAE).
   • Compared predictions with actual housing prices to evaluate accuracy.

4. Random Forest Capabilities:

   • Demonstrated the strength of Random Forest in handling complex feature interactions and reducing overfitting through ensemble decision trees.

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📜 License

This project is licensed under the MIT License, allowing free usage for educational and non-commercial purposes.

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🌐 Connect with Me

If you'd like to connect, collaborate, or provide feedback, feel free to reach out:

• LinkedIn: Vijay Mahawar
• GitHub: vmahawar